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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 19, Iss. 27 — Dec. 19, 2011
  • pp: 26816–26826

Use of weighting algorithms to improve traditional support vector machine based classifications of reflectance data

Bin Qi, Chunhui Zhao, Eunseog Youn, and Christian Nansen  »View Author Affiliations


Optics Express, Vol. 19, Issue 27, pp. 26816-26826 (2011)
http://dx.doi.org/10.1364/OE.19.026816


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Abstract

Support vector machine (SVM) is widely used in classification of hyperspectral reflectance data. In traditional SVM, features are generated from all or subsets of spectral bands with each feature contributing equally to the classification. In classification of small hyperspectral reflectance data sets, a common challenge is Hughes phenomenon, which is caused by many redundant features and resulting in subsequent poor classification accuracy. In this study, we examined two approaches to assigning weights to SVM features to increase classification accuracy and reduce adverse effects of Hughes phenomenon: 1) “RSVM” refers to support vector machine with relief feature weighting algorithm, and 2) “FRSVM” refers to support vector machine with fuzzy relief feature weighting algorithm. We used standardized weights to extract a subset of features with high classification contribution. Analyses were conducted on a reflectance data set of individual corn kernels from three inbred lines and a public data set with three selected land-cover classes. Both weighting methods and reduction of features increased classification accuracy of traditional SVM and therefore reduced adverse effects of Hughes phenomenon.

© 2011 OSA

OCIS Codes
(100.0100) Image processing : Image processing
(280.0280) Remote sensing and sensors : Remote sensing and sensors

ToC Category:
Image Processing

History
Original Manuscript: August 16, 2011
Revised Manuscript: December 1, 2011
Manuscript Accepted: December 1, 2011
Published: December 15, 2011

Citation
Bin Qi, Chunhui Zhao, Eunseog Youn, and Christian Nansen, "Use of weighting algorithms to improve traditional support vector machine based classifications of reflectance data," Opt. Express 19, 26816-26826 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-27-26816


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